House prices,bank instability,and economic growth: Evidence from the threshold model

This paper examines the effects of house prices on bank instability when gauged at various levels of income growth. Bank stability may respond differently to house price changes or deviations from fundamental values in an economic boom environment than in a bust circumstance. A threshold estimation technique developed by Hansen (1999) is applied to a panel of 286 U.S. Metropolitan Statistical Areas (MSAs) over the period 1990Q1–2010Q4. We consider two house price indicators: the house price changes and the house price deviations from long-run equilibrium. The results suggest the existence of income growth threshold effects in the relationship between house prices and bank instability. Specifically, there are two income growth thresholds when using the house price changes and one income growth threshold when the house price deviations are applied. Robustness results using the non-MSAs sample from 1995Q1 to 2010Q4 provide further evidence of income growth threshold effects.     

Boom–Bust Cycles and the Forecasting Performance of Linear and Non-Linear Models of House Prices

The tremendous rise in house prices over the last decade has been both a national and a global phenomenon. The growth of secondary mortgage holdings and the increased impact of house prices on consumption and other components of economic activity imply ever-greater importance for accurate forecasts of home price changes. Given the boom–bust nature of housing markets, nonlinear techniques seem intuitively very well suited to forecasting prices, and better, for volatile markets, than linear models which impose symmetry of adjustment in both rising and falling price periods. Accordingly, Crawford and Fratantoni (Real Estate Economics 31:223–243, 2003) apply a Markov-switching model to U.S. home prices, and compare the performance with autoregressive-moving average (ARMA) and generalized autoregressive conditional heteroscedastic (GARCH) models. While the switching model shows great promise with excellent in-sample fit, its out-of-sample forecasts are generally inferior to more standard forecasting techniques. Since these results were published, some researchers have discovered that the Markov-switching model is particularly ill-suited for forecasting. We thus consider other non-linear models besides the Markov switching, and after evaluating alternatives, employ the generalized autoregressive (GAR) model. We find the GAR does a better job at out-of-sample forecasting than ARMA and GARCH models in many cases, especially in those markets traditionally associated with high home-price volatility.     

Hedging House Price Risk with CME Futures Contracts: The Case of Las Vegas Residential Real Estate

Until the recent introduction of real estate futures on the Chicago Mercantile Exchange (CME), there have been few opportunities to manage house price risk. This paper examines whether house price risk can be effectively hedged in Las Vegas, one of the CME contract cities. The analysis considers hedging from the viewpoint of real estate investment groups, mortgage portfolio investors, builder/developers and individual homeowners. For investment groups and mortgage holders holding a mix of new and existing home assets, CME futures would have reduced house price risk by more than 88% over the 1994–2006 period. Similarly, homeowners implicitly hedging price volatility of existing homes also would have fared well over the sample period. However, builder/developers worried about new home price appreciation would have been much less successful in managing their risk. One important caveat, minimum variance hedge ratios change over time and may cause hedge performance to suffer.

How does house price influence monetary policy transmission?

The literature has focused on house price growth to explain the inefficiency of monetary policy. From the perspective of substitution, this paper explains the relationship between house prices and monetary policy considering not only house price growth but also house price uncertainty. By constructing a theoretical model including both financial-asset and fixed-asset investment, we find that expansionary monetary policy not only promotes total investment but simultaneously also leads to substitution towards financial assets. However, a rise in house price growth or house price uncertainty will mitigate the substitution effect generated by monetary policy. These propositions are supported by empirical data on China's nonfinancial listed firms from 2009 Q1 to 2018 Q3 and the results are robust to a variety of model specifications and empirical approaches. Our findings imply that real estate regulatory policy should coordinate with monetary policy in maximizing fixed-asset investment.     

Seemingly Irrational but Predictable Price Formation in Seoul’s Housing Market

This paper tests the dynamics implied by a supplied-constrained view of the relationship between market fundamentals and house prices in the case of Seoul’s condominium market. The view is that supply constraints have led to serious shortages in certain submarkets, and that these shortages have led to a rapid rise in house prices and to panic buying or inflation-induced investing and to further price increases. The estimation period of the test is November 1988–February 2007. The results suggest that house prices in Seoul are highly persistent because of these supply constraints. Additionally, we do what we can with the available data to determine if house price increases serve to increase demand further, and if rent-price ratios and nominal interest rates are a good predictor of how housing prices in Seoul will evolve over time.     

Repeat Sales Index for Thin Markets

The repeat sales model is commonly used to construct reliable house price indices in absence of individual characteristics of the real estate. Several adaptations of the original model by Bailey et al. (J Am Stat Assoc 58:933–942, 1963) are proposed in literature. They all have in common using a dummy variable approach for measuring price indices. In order to reduce the impact of transaction price noise on the estimates of price indices, Goetzmann (J Real Estate Finance Econ 5:5–53, 1992) used a random walk with drift process for the log price levels instead of the dummy variable approach. The model that is proposed in this article can be interpreted as a generalization of the Goetzmann methodology. We replace the random walk with drift model by a structural time series model, in particular by a local linear trend model in which both the level and the drift parameter can vary over time. An additional variable—the reciprocal of the time between sales—is included in the repeat sales model to deal with the effect of the time between sales on the estimated returns. This approach is robust can be applied in thin markets where relatively few selling prices are available. Contrary to the dummy variable approach, the structural time series model enables prediction of the price level based on preceding and subsequent information, implying that even for particular time periods where no observations are available an estimate of the price level can be provided. Conditional on the variance parameters, an estimate of the price level can be obtained by applying regression in the general linear model with a prior for the price level, generated by the local linear trend model. The variance parameters can be estimated by maximum likelihood. The model is applied to several subsets of selling prices in the Netherlands. Results are compared to standard repeat sales models, including the Goetzmann model.     

Components of Market Risk and Return

This article proposes a flexible but parsimonious specificationof the joint dynamics of market risk and return to produce forecastsof a time-varying market equity premium. Our parsimonious volatilitymodel allows components to decay at different rates, generatesmean-reverting forecasts, and allows variance targeting. Thesefeatures contribute to realistic equity premium forecasts forthe U.S. market over the 1840–2006 period. For example,the premium forecast was low in the mid-1990s but has recentlyincreased. Although the market's total conditional variancehas a positive effect on returns, the smooth long-run componentof volatility is more important for capturing the dynamics ofthe premium. This result is robust to univariate specificationsthat condition on either levels or logs of past realized volatility(RV), as well as to a new bivariate model of returns and RV.     

Regional Housing Prices in the USA: An Empirical Investigation of Nonlinearity

Existing literature on housing prices is predominantly in a linear framework, and an important question that has not been addressed is whether housing prices exhibit nonlinearity. We examine Smooth Transition Autoregressive (STAR) model based nonlinear properties of housing prices over the 1969–2004 period for the entire US and the four regions. Our main findings are (1) housing price for the entire US and all regions except for the Midwest show non-linearity, (2) the dynamic properties implied by the nonlinear estimation explain the typical patterns that have characterized each housing market, and (3) results of Granger causality tests look more plausible in the nonlinear framework where we find stronger evidence of Granger causality from housing price to employment and also from mortgage rates to housing price.

Comparison of Option Prices in Semimartingale Models

In this paper we generalize the recent comparison results of El Karoui et al. (Math Finance 8:93–126, 1998), Bellamy and Jeanblanc (Finance Stoch 4:209–222, 2000) and Gushchin and Mordecki (Proc Steklov Inst Math 237:73–113, 2002) to d-dimensional exponential semimartingales. Our main result gives sufficient conditions for the comparison of European options with respect to martingale pricing measures. The comparison is with respect to convex and also with respect to directionally convex functions. Sufficient conditions for these orderings are formulated in terms of the predictable characteristics of the stochastic logarithm of the stock price processes. As examples we discuss the comparison of exponential semimartingales to multivariate diffusion processes, to stochastic volatility models, to Lévy processes, and to diffusions with jumps. We obtain extensions of several recent results on nontrivial price intervals. A crucial property in this approach is the propagation of convexity property. We develop a new approach to establish this property for several further examples of univariate and multivariate processes.     

Explaining House Price Dynamics: Isolating the Role of Nonfundamentals

This paper examines the role of nonfundamentals‐based sentiment in house price dynamics, including the well‐documented volatility and persistence of house prices during booms and busts. To measure and isolate sentiment's effect, we employ survey‐based indicators that proxy for the sentiment of three major agents in housing markets: home buyers (demand side), home builders (supply side), and lenders (credit suppliers). After orthogonalizing each sentiment measure against a broad set of fundamental variables, we find strong and consistent evidence that the changing sentiment of all three sets of market participants predicts house price appreciation in subsequent quarters, above and beyond the impact of changes in lagged price changes, fundamentals, and market liquidity. More specifically, a one‐standard‐deviation shock to market sentiment is associated with a 32–57 basis point increase in real house price appreciation over the next two quarters. These price effects are large relative to the average real price appreciation of 71 basis points per quarter observed over the full sample period. Moreover, housing market sentiment and its effect on real house prices is highly persistent. The results also reveal that the dynamic relation between sentiment and house prices can create feedback effects that contribute to the persistence typically observed in house price movements during boom and bust cycles.     

The Market for New Issues: Impact of Offering Price on Price Support and Underpricing

Previous studies have shown that the pattern of first day returns to initialpublic offerings is consistent with the hypotheses of underpricing and price support. We examine two different periods, 1975–1984 and 1996–2002, and find that in each case the measures of price support and underpricing are substantially affected by the initial public offerings' beginning price. During the period 1975–1984, the mean and standard deviation of returns to the price supported group are nearly always zero regardless of price, whileg the mean of the returns to the underpriced group is smile-shaped: high for low-priced and high-priced stocks but lower for stocks offered at intermediate prices. The patterns are different in the most recent data: the mean and standard deviation of both the price supported and underpriced groups are smile-shaped. For the lowest priced stocks, the measures in the later period mirror those for the 1975–1984 period, but for more expensive stocks the measures are substantially higher. The results apply to the first day returns of both firm commitment and best efforts offerings. Once price is taken into account, other than the difference in the probability of price support, the differences among offering types seem to be of secondary importance in explaining first day returns. JEL Classification: 1, G12, G24     

House Prices and Bubbles in New Zealand

This paper studies actual (real) house prices relative to fundamental (real) house values in New Zealand for the period 1970–2005. Utilizing a dynamic present value model, we find disparities between actual and fundamental house prices in the early 1970s and 1980s and from 2000 to date. We model the bubble component that is related to fundamentals (the intrinsic component), making it possible to highlight whether a bubble still exists after that component is accounted for. We then analyze any remaining bubble to detect any momentum behavior. Much of the overvaluation of the housing market is found to be due to price dynamics rather than an overreaction to fundamentals.

Macro information environment change and the quality of management earnings forecasts

The 1990s were characterized by substantial increases in the performance of and investor reliance on financial analysts. Because managers possess superior private information and issue forecasts to align investors’ expectations with their own, we predict that managers increased the quality of their earnings forecasts during the 1990s in order to keep pace with the improved forward-looking information provided by financial analysts, upon which investors increasingly relied. Using a sample of 2,437 management earnings forecasts, we document an increase in management earnings forecast precision, management earnings forecast accuracy, and managers’ tendency to explain earnings forecasts in 1993–1996 relative to 1983–1986. Given that these forecast characteristics are linked to greater informativeness and credibility, we also document that the information content of management earnings forecasts, as measured by the strength of share price responses to forecast news, increased in 1993–1996 relative to 1983–1986. As expected, the increased information content of management forecasts primarily occurred for firms covered by financial analysts.

Factors driving aggregate mortality rates in postwar Germany

Using German data over the period 1956–2006, this study provides a comprehensive empirical analysis of factors driving aggregate mortality rates over time. It differs from previous contributions in this field by simultaneously considering an extensive set of macroeconomic, socioeconomic, and ecological factors as explanatory variables. Our regression analysis shows that sex- and age-specific mortality rates vary substantially in their response to external factors. Strongest associations are found with changes in real GDP, flu epidemics, and the two lifestyle variables—alcohol and cigarette consumption—in both univariate and multivariate setups. Further analysis indicates that these effects are primarily contemporary, whereas other indicators, such as weather conditions, exert lagged effects. We derive optimal multivariate models for every age group that provide a good fit to the observed variation in annual mortality rates, and thereby confirm the relevance of the identified factors.     

Forecasting exchange rates out-of-sample with panel methods and real-time data

This paper evaluates out-of-sample exchange rate forecasting with Purchasing Power Parity (PPP) and Taylor rule fundamentals for 9 OECD countries vis-à-vis the U.S. dollar over the period from 1973:Q1 to 2009:Q1 at short and long horizons. In contrast with previous work, which reports “forecasts” using revised data, I construct a quarterly real-time dataset that incorporates only the information available to market participants when the forecasts were made. Using bootstrapped out-of-sample test statistics, the exchange rate model with Taylor rule fundamentals performs better at the one-quarter horizon and panel estimation is not able to improve its performance. The PPP model, however, forecasts better at the 16-quarter horizon and its performance increases in panel framework. The results are in accord with previous research on PPP and Taylor rule models.     

The Time-Series Properties of House Prices: A Case Study of the Southern California Market

We examine the time-series relationship between house prices in eight Southern California metropolitan statistical areas (MSAs). First, we perform cointegration tests of the house price indexes for the MSAs, finding seven cointegrating vectors. Thus, the evidence suggests that one common trend links the house prices in these eight MSAs, a purchasing power parity finding for the house prices in Southern California. Second, we perform temporal Granger causality tests. The Santa Anna MSA temporally causes house prices in six of the other seven MSAs, excluding only the San Luis Obispo MSA. The Oxnard MSA experiences the largest number of temporal effects from six of the seven MSAs, excluding only Los Angeles. The Santa Barbara MSA proves the most isolated. It temporally causes house prices in only two other MSAs (Los Angeles and Oxnard) and house prices in the Santa Anna MSA temporally cause prices in Santa Barbara. Third, we calculate out-of-sample forecasts in each MSA, using various vector autoregressive and vector error-correction models, as well as Bayesian, spatial, and causality versions of these models with various priors. Different specifications provide superior forecasts in the different MSAs. Finally, we consider how theses time-series models can predict out-of-sample peaks and declines in house prices after in 2005 and 2006. Recursive forecasts, where we update the sample each quarter, provide reasonably good forecasts of the peaks and declines of the house price indexes.     

The US Housing Market: Asset Pricing Forecasts Using Time Varying Coefficients

The US housing market has experienced significant cyclical volatility over the last twenty-five years due to major structural changes and economic fluctuations. In addition, the housing market is generally considered to be weak form inefficient. Houses are relatively illiquid, exceptionally heterogeneous, and are associated with large transactions costs. As such, past research has shown that it is possible to predict, at least partially, the time path of housing prices. The ability to predict housing prices is important such that investors can make better asset allocation decisions, including the pricing and underwriting of mortgages. Most of the prior studies examining the US housing market have employed constant coefficient approaches to forecast house price movements. However, this approach is not optimal as an examination of data reveals substantial sub-sample parameter instability. To account for the parameter instability, we employ alternative estimation methodologies where the estimated parameters are allowed to vary over time. The results provide strong empirical evidence in favor of utilizing the rolling Generalized Autoregressive Conditional Heteroskedastic (GARCH) Model and the Kalman Filter with an Autoregressive Presentation (KAR) for the parameters time variation. Lastly, we provide out-of-sample forecasts and demonstrate the precision of our approach.     

Pricing Property Index Linked Swaps with Counterparty Default Risk

This paper extends Bjork and Clapham (Journal of Housing Economics 11:418–432, 2002) model for pricing real estate index total return swaps. Our extension considers counterparty default risk within a first passage contingent claims model. We price total return swaps on property indices with different levels of default risk. We develop this model under same assumptions as Bjork and Clapham (Journal of Housing Economics 11:418–432, 2002) and find that total return swap price is no longer zero. Total return swap payer must charge a spread over the market interest rate that compensates him for the exposure to this additional risk. Based on commercial property indices in the UK, we observe that computed spreads are much lower than a sample of quotes obtained from one of the traders in the market.     

Generalised Rational Bias in Financial Forecasts

Laster et al. (Q J Econ 114(1):293–318, 1999) built an economic model in which forecasters have incentives to generate forecasts that differ form the consensus. It is shown that the dispersion of the equilibrium distribution of forecasters, depends on the relative importance given on the intensive forecast users’ loss versus the publicity gain from occasional users. These results depend heavily on the assumption of symmetry for the loss and density functions. In this paper we examine the effects of generalising loss preferences and probability densities to allow for asymmetries through the LinEx loss and the Skewed Normal density, respectively. We derive the generalised equilibrium distribution of forecasts which contains the results of Laster et al. as a special case. The presence of asymmetric preferences is shown to cause a movement of the distribution away from the conditional mean, towards the optimal forecast under loss asymmetry. Furthermore, forecasts now tend to cluster around this quantity in an asymmetric way. These effects tend to be further strengthened or partially offset by the presence of skewness in the distribution of data, a result consistent with the conclusions of Christodoulakis (Finan Res Lett 2:227–233, 2005).The author is grateful to an anonymous referee for helpful comments that have improved the paper. The views expressed in the paper are those of the author and should in no part be attributed to the Bank of Greece.     

Fundamental Real Estate Prices: An Empirical Estimation with International Data

We propose two alternative models to estimate fundamental prices on real estate markets. The first model is based on a no-arbitrage condition between renting and buying. The second model interprets the period costs as the result of market equilibrium between housing demand and supply. We estimate both models for the USA, the UK, Japan, Switzerland, and the Netherlands. We find that observed prices deviate substantially and for long periods from their estimated fundamental values. However, we find some evidence that, in the long-run, actual prices tend to return to their fundamental values progressively. This result is due to both impulse–response functions and forecast analyses. In particular, we find that using the fundamental price significantly increases the accuracy of out-of-sample long-term forecasts of the price.